MGMT 675



AI-Assisted Financial Analysis

Visualization

Exercise 1

  • metrics_5year.xlsx
  • tickers.xlsx
  • Download from course website and upload to Julius.

Explore data

  • How many rows in the tickers dataset? What are the columns?
  • What are the columns in metrics_5year?
  • Group metrics_5year by date and count the number of tickers at each date.

Merge

  • Do an inner merge of tickers and metrics_5year on ticker.
  • Group by date and count the number of tickers at each date.
  • Save the merged dataset as merged.xlsx.

Explore visually

  • We want to see how the market has changed and how the metrics vary across sectors.
  • Suggestions:
    • Filter to the date 2023-12-29, group by sector and generate barplots of aggregates (total or median) - e.g., total marketcap or median pb.
    • Group by (sector, date) and generate 3d barplots of aggregates.
    • Group by (sector, date) and generate heatmaps of aggregates.

Exercise 2

  • Ask Julius to use yfinance to get closing prices for CVX during April 2020 (if error, ask to pip install yfinance==0.1.70).
  • Ask Julius to use pandas datareader to get crude oil prices from FRED during April 2020.
  • Ask Julius to plot the CVX prices and crude oil prices in the same figure with the crude price labels on a second y axis.

Exercise 3

  • Ask Julius to get the histories since 1990 of the 3-month, 1-year, 5-year, and 10-year Treasury yields from FRED.
  • Ask Julius to downsample to first day of the month.
  • Ask Julius to use plotly to create an animation with the yield on the y axis and the time to maturity on the x axis and using the date as the animation frame.
  • Ask Julius to save it as html.